Problems that involve combinations of multiple variables that can be manipulated independently are typically solved with processing-intensive solution methods. Such a problem may be referred to as a logistical problem, where the problem does not have a solution consisting of a simple yes or no, or even an answer to a numeric calculation. Rather, a set of solutions can be generated that satisfies each of the variables in different combinations, creating multiple potential solutions. A problem solving system may include an algorithm that generates potential solutions to a logistical problem. As a general rule, the solutions to logistical problems are based on heuristics, for example, where certain assumptions and/or generalizations are made regarding an aspect of the logistical problem.
As an example, consider supply chain management (SCM), where the logistics of the SCM includes planning and implementing the movement and storage of goods or services. A supply chain is a network of retailers, distributors, transportation service providers, warehouses, and suppliers that take part in the production, delivery and sale of goods or services. The movement of goods and services through a supply chain may include shipment or transportation of goods from one location to another. The shipment of goods involves one or more vehicles such as trucks, ships, trains, or airplanes, and involves the planning of the arrangement of the goods to be shipped in the vehicle. The shipment of goods may involve complex constraints, which are evaluated by the SCM logistics.
The goal of logistics is creating effective solutions to movement and storage problems, knowing that “perfect” solutions may be infeasible given the number of considerations or variables at play. For example, in goods transportation, the movement of the goods involves, among other things, selecting a schedule for shipment, selecting a carrier, and loading the goods into the carrier's vehicles. Each aspect of this problem may be a logistical problem, or logistical sub-problem to the original logistical problem of transporting the goods. The SCM system may generalize or ignore certain constraints or conditions to simulate another. For example, an SCM system may use generalized conditions for arrangement of goods for shipping (e.g., ignoring whether crates used are stackable, or ignoring specific geometries of shipping vehicles available). While ignoring or not considering certain specific conditions may be tolerable in certain instances, it may not be desirable in other instances. For example, certain unconsidered conditions may be considered significant in certain contexts (e.g., generalizing costs of shipping instead of factoring exact shipping costs may be considered to generalize or ignore an important consideration).
Generalizing certain considerations as a rule may result in a problem solving system that fails to consider a condition deemed significant to a user of the system. Not having the ability to consider certain conditions may make the problem solving system less useful.
Descriptions of certain details and implementations follow, including a description of the figures, which may depict some or all of the embodiments described below, as well as discussing other potential embodiments or implementations of the inventive concepts presented herein. An overview of embodiments of the invention is provided below, followed by a more detailed description with reference to the drawings.